CRUST | Browsing by Author "Pakhomova, Victoria M."

Browsing by Author "Pakhomova, Victoria M."

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Databases : methodical recommendations for individual task
(Ukrainian State University of Science and Technologies, Dnipro, 2022) Pakhomova, Victoria M.
ENG: Methodological recommendations are aimed at preparing and doing individual tasks in the discipline «Databases» for foreign applicants of Bachelor’s Degree of specialties 123 «Computer Engineering» and 125 «Cybersecurity».
Design of Databases by Bachelor’s Degree Applicants when Writing a Qualification Paper
(Kupriyenko SV in conjunction with KindleDP, USA, Seattle, 2023) Pakhomova, Victoria M.
ENG: For use by applicants for a bachelor's degree when writing qualification papers, the «BachelorDesignDB» methodology is proposed, which consists of the following stages: review of sources on existing databases; study of the subject area in order to form an initial attitude; database design using well-known methods («Normal Forms» and «Essence-Relation») and analysis of the design results obtained; creation of a designed database with the help of the selected software application and its protection; optimization and performance improvement of the created database; formulating conclusions and providing recommendations for the practical use of the created database.
Detection of Attacks of the U2R Category by Means of the SOM on Database NSL-KDD
(Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», ІВК «Системні технології», Дніпро, 2022) Pakhomova, Victoria M.; Mehelbei, Yehor O.
ENG: Creating an effective system for detecting network attacks requires the use of qualitatively new approaches to information processing, which should be based on adaptive algorithms capable of self-learning. The mathematical apparatus of the Kohonen self-organizing map (SOM) was used as a research method. Python language with a wide range of modern standard tools was used as a software implementation of the Kohonen SOM addition, this section compiles the Python software model «SOM_U2R» using a Kohonen SOM. Created «SOM_U2R» software model on database NSL-KDD an error research was performed for different number of epochs with different map sizes. On the «SOM_U2R» model the research of parameters of quality of detection of attacks is carried out. It is determined that on the «SOM_U2R» created software model the error of the second kind of detection of network classes of attacks Buffer_overflow and Rootkit is 6 %, and for the class Loadmodule reached 16 %. In addition, a survey of the Fmeasure was conducted for a different number of epochs of learning the Kohonen SOM. It is determined that for all network attack classes (except Buffer_overflow) the F-measure increases, reaching its maximum value at 50 epochs.
Detection of Attacks on a Computer Network Based on the Use of Neural Networks Complex
(Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2020) Zhukovyts’kyy, Igor V.; Pakhomova, Victoria M.; Ostapets, Denis O.; Tsyhanok, O. I.
ENG: Purpose. The article is aimed at the development of a methodology for detecting attacks on a computer network. To achieve this goal the following tasks were solved: to develop a methodology for detecting attacks on a computer network based on an ensemble of neural networks using normalized data from the open KDD Cup 99 database; when performing machine training to identify the optimal parameters of the neural network which will provide a sufficiently high level of reliability of detection of intrusions into the computer network. Methodology. As an architectural solution of the attack detection module, a two-level network system is proposed, based on an ensemble of five neural networks of the multilayer perceptron type. The first neural network to determine the category of attack class (DoS, R2L, U2R, Probe) or the fact that there was no attack; other neural networks – to detect the type of attack, if any (each of these four neural networks corresponds to one class of attack and is able to identify types that belong only to this class). Findings. The created software model was used to study the parameters of the neural network configuration 41–1–132–5, which determines the category of the attack class on the computer network. It is determined that the optimal training speed is 0.001. The ADAM algorithm proved to be the best for optimization. The ReLU function is the most suitable activation function for the hidden layer, and the hyperbolic tangent function – for the output layer activation function. Accuracy in test and validation samples was 92.86 % and 91.03 %, respectively. Originality. The developed software model, which uses the Python 3.5 programming lan-guage, the integrated development environment PyCharm 2016.3 and the Tensorflow 1.2 framework, makes it pos-sible to detect all types of attacks of DoS, U2R, R2L, Probe classes. Practical value. Graphical dependencies of accuracy of neural networks at various parameters are received: speed of training; activation function; optimization algorithm. The optimal parameters of neural networks have been determined, which will ensure a sufficiently high level of reliability of intrusion detection into a computer network.
Detection of U2R Attacks by Means of a Multilayer Neural
(Sworld & D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, 2024) Pakhomova, Victoria M.; Mostynets, Vladyslav L.
ENG: As a research method, multi layer neural network (MLNN) configurations 41-1-Х-4 were used, where 41 is the number of input neurons; 1 – the number of hidden layers; X – the number of hidden neurons; 4 – the number of resultant neurons created using the Neural Network Toolbox of the MatLAB system, to detect U2R network attacks: y1 – Rootkit attack, y2 –Buffer_overflow attack, y3 – Loadmodule attack, y4 – No attack. Using the open database of NSL-KDD network traffic parameters on the created MLNN, a study of its error and number of epochs at different number of hidden neurons (25, 35 and 45 was carried out using different training algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradient. It is determined that the smallest value of the MLNN error was based on the use of the hyperbolic tangent as a function of activating a hidden layer according by the Levenberg-Marquardt training algorithm, and it is enough to have 25 hidden neurons. An assessment of the quality of detection of U2R attacks on MLNN configuration 41-1-25-4 at its optimal parameters was carried out. It is determined that errors of the first and second kind are 9 % and 10 %, respectively.
Determination of the Optimal Parameters of Wireless Local Network on the Created Program Using the Ant Algorithm
(ProConference in conjunction with KindleDP Seattle, Washington, USA, 2022) Pakhomova, Victoria M.; Salohub, Maksym V.
ENG: The «WLAN_EliteAS» program, created in the JavaScript language of the ant algorithm, determines the optimal number of base stations of wireless local networks and their location on the territory of USUST. Initial data of the «WLAN_EliteAS» program: parameters of the territory of USUST (coordinates of vacant places; number of clients that need to be connected to base stations); wireless local network parameters (base station coverage radius, maximum number of clients to one base station); parameters of the ant algorithm (number of ordinary and elite ants, irrigation and evaporation, greed and laziness). The quality of the obtained solutions depends significantly on the choice of parameters of the ant algorithm.
Forecasting Network Traffic in the Information and Telecommunication System of Railway Transport by Means of a Neural Network
(MATEC Web of Conferences, 2023) Zhukovytskyy, Igor V.; Pakhomova, Victoria M.
ENG: Network traffic is one of the most important actual indicators of the information and telecommunication system (ITS) of railway transport. Recent studies show that network traffic in the ITS of railway transport is self-similar (fractal), for the study of which the Hirst indicator can be used. One of the possible solutions is a method of network traffic forecasting using neural network technology, which will allow you to manage traffic in real time, avoid server overload and improve the quality of services, which confirms the relevance of this topic. The method of forecasting the parameters of network traffic in the ITS of railway transport using neural network technology is proposed: for long-term forecasting (day-ahead) of network traffic volume based on network traffic volumes for the previous three days using the created multilayer neuro-fuzzy network; for short-term prediction (one step forward, which takes five minutes) of network traffic intensity based on network traffic intensities for the previous fifteen minutes using the created multilayer neural network. The corresponding samples are formed on the basis of real values of network traffic parameters in the ITS of railway transport. Studies of optimal parameters of the created multilayer neural network, which can be integrated into specialized analytical servers of the ITS of railway transport, are carried out, which will provide a sufficiently high level of short-term forecasting of network traffic parameters (in particular intensity) in the ITS of railway transport at the stage of deepening the integration of the national transport network into the Trans-European Transport Network.
Formation of Competencies Among Applicants of Foreign Origin in Blended Learning «Local Networks» Discipline
(Sergeieva&Co, Germany, Karlsruhe, 2022) Pakhomova, Victoria M.
ENG: The proposed «BlenLearnEnglLAN» methodology for the formation of competences among applicants of foreign origin bachelor's degree in the «Computer Engineering» specialty during blended learning in the «Local Networks» discipline: 1) study of basic concepts and fundamental principles of various network technologies during lectures held using the «Zoom» system; 2) compilation of the structure of the local network and assessment of its correctness, according to the compiled structure, creation of a simulation model of the local network in NetCracker Pro and conducting research on it during laboratory work carried out face-to-face; 3) research of network traffic parameters using neural network technology and obtained data on a simulation model during independent work using recommended sources; 4) development of theoretical material using the lecturer's presentations and testing in the «Leader» system, arguing the choice of network technology based on the obtained results of research on simulation models.
Formation of Competencies and Soft Skills when Performing Brigade Discipline Tasks «Mathematical Foundation of Information Security»
(Sergeieva&Co, Karlsruhe, Germany, 2024) Pakhomova, Victoria M.
ENG: The proposed methodology of "SoftSkillsMathFIS" for the formation of competencies of applicants for a bachelor's degree in blended learning in the discipline "Mathematical Foundations of Information Security": 1) study of mathematical concepts (symbols Legendre and Jacobi, their properties) during lectures conducted with the help of Zoom system, 2) algorithmization and programming for the implementation of the Solovey-Strassen test and the organization of relevant research during laboratory work, 3) acquisition of practical skills in using probabilistic tests to determine the primality of a number on based on various mathematical approaches and tools when performing independent work with use of recommended sources, 4) elaboration of theoretical material on using the lecturer's presentations and passing testing in the "Lider" system.
Formation of Competencies in Applicants of the Bachelor’s Degree of Foreign Origin in Distance Learning in the «Database» Discipline
(Sergeieva&Co, Germany, Karlsruhe, 2022) Pakhomova, Victoria M.
ENG: The «ForeignDistLearnDB» methodology on the formation of competencies of applicants for foreign origin «Bachelor» in «Computer Engineering» in distance learning in the «Databases» discipline, consisting of the following stages: 1) familiarization with the basic models of data representation (during lectures); 2) study of DDL, DML and DQL constructs that form the basis of SQL (during laboratory work); 3) designing a relational database using the «Normal Forms» and «Essence-Relation» methods (during the individual task); 4) analysis of the process and results of database design by different methods (mathematical and graphical); 5) elaboration of theoretical material with the use of lecturer presentations and modular testing in the «Lider» system.
Identifying Threats in Computer Network Based on Multilayer Neural Network
(Дніпропетровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2018) Zhukovyts’kyy, Igor V.; Pakhomova, Victoria M.
ENG: Purpose. Currently, there appear more often the reports of penetration into computer networks and attacks on the Web-server. Attacks are divided into the following categories: DoS, U2R, R2L, Probe. The purpose of the article is to identify threats in a computer network based on network traffic parameters using neural network technology, which will protect the server. Methodology. The detection of such threats as Back, Buffer_overflow, Quess_password, Ipsweep, Neptune in the computer network is implemented on the basis of analysis and processing of data on the parameters of network connections that use the TCP/IP protocol stack using the 19-1-25-5 neural network configuration in the Fann Explorer program. When simulating the operation of the neural network, a training (430 examples), a testing (200 examples) and a control sample (25 examples) were used, based on an open KDDCUP-99 database of 500000 connection records. Findings. The neural network created on the control sample determined an error of 0.322. It is determined that the configuration network 19-1-25-5 copes well with such attacks as Back, Buffer_overflow and Ipsweep. To detect the attacks of Quess_password and Neptune, the task of 19 network traffic parameters is not enough. Originality. We obtained dependencies of the neural network training time (number of epochs) on the number of neurons in the hidden layer (from 10 to 55) and the number of hidden layers (from 1 to 4). When the number of neurons in the hidden layer increases, the neural network by Batch algorithm is trained almost three times faster than the neural network by Resilient algorithm. When the number of hidden layers increases, the neural network by Resilient algorithm is trained almost twice as fast as that by Incremental algorithm. Practical value. Based on the network traffic parameters, the use of 19-1-25-5 configuration neural network will allow to detect in real time the computer network threats Back, Buffer_overflow, Quess_password, Ipsweep, Neptune and to perform appropriate monitoring.
Intelligent Routing in the Network of Information and Telecommunication System of Railway Transport
(Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2019) Pakhomova, Victoria M.; Skaballanovich, Tetiana I.; Bondareva, Valentyna S.
ENG: Purpose. At the present stage, the strategy of informatization of railway transport of Ukraine envisages the tran-sition to a three-level management structure with the creation of a single information space, therefore one of the key tasks remains the organization of routing in the network of information and telecommunication system (ITS) of railway transport. In this regard, the purpose of the article is to develop a method for determining the routes in the network of information and telecommunication system of railway transport at the trunk level using neural network technology. Methodology. In order to determine the routes in the network of the information and telecommunica-tion system of railway transport, which at present is working based on the technologies of the Ethernet family, one should create a neural model 21-1-45-21, to the input of which an array of delays on routers is supplied; as a result vector – build tags of communication channels to the routes. Findings. The optimal variant is the neural network of configuration 21-1-45-21 with a sigmoid activation function in a hidden layer and a linear activation function in the resulting layer, which is trained according to the Levenberg-Marquardt algorithm. The most quickly the neural net-work is being trained in the samples of different lengths, it is less susceptible to retraining, reaches the value of the mean square error of 0.2, and in the control sample determines the optimal path with a probability of 0.9, while the length of the training sample of 100 examples is sufficient. Originality. There were constructed the dependencies of mean square error and training time (number of epochs) of the neural network on the number of hidden neurons ac-cording to different learning algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradi-ent on samples of different lengths. Practical value. The use of a multilayered neural model, to the entry of which the delay values of routers are supplied, will make it possible to determine the corresponding routes of transmission of control messages (minimum value graph) in the network of information and telecommunication system of railway transport at the trunk level in the real time.
Investigation of Multilayer Neural Network Parameters for Determination of R2l Category Network Attacks
(Sergeieva&Co, Karlsruhe, Germany, 2021) Pakhomova, Victoria M.; Bikovska, Daria H.
ENG: To determine R2L network attacks, Python created the MLP software model using the open KDDCup database, which was used to study the values of accuracy and error from the number of neural network learning epochs based on various data: activation functions, hidden neurons, optimization methods. The optimal parameters and configuration of the neural network for detecting classes of network attacks are determined: Ftp_write, Guess_passwd, Imap, Multihop, Phf, Spy, Warezclient, Warezmaster.
Investigation of the pPssibility of using Neurofuzzy Network to Determine the Extent of DoS Attack
(Sworld & D.A. Tsenov Academy of Economics – Svishtov, Bulgaria, 2023) Pakhomova, Victoria M.; Kovalov, Rodion
ENG: As a research method, ANFIS configurations 4-5-8-16-16-1 were used, where 4 is the number of input neurons; 5 – total number of layers; 8 – the number of neurons of the first hidden layer; 16 – the number of neurons of the second hidden layer; 16 – the number of neurons of the third hidden layer; 1 – the number of resultant neurons created using the Fuzzy Logic Toolbox of the MatLAB system, the resulting characteristic is the degree of confidence that the DoS attack occurred at the following terms: low; medium; high. Using the open database of NSL-KDD network traffic parameters on the created ANFIS, a study of its error at different affiliation functions on samples of different lengths was carried out using different methods of training optimization. It is determined that the smallest value of the ANFIS error was based on the use of the multiparameter Bell function by the Hybrid learning optimization method, and it is enough to have a training sample of 70 examples.
Local Networks : methodical recommendations for laboratory works
(Ukrainian State University of Science and Technologies, Dnipro, 2022) Pakhomova, Victoria M.; Miroshnychenko, Iryna H.
ENG: Methodological recommendations are aimed at preparing and doing individual laboratory tasks in the discipline «Local Networks» for foreign applicants of Bachelor’s Degree of specialties 123 «Computer Engineering» and 125 «Cybersecurity».
Methodology for the Formation of Competences of First Degree Holders in the Discipline «Mathematical Foundation of Information Security»
(Sergeieva&Co, Karlsruhe, 2023) Pakhomova, Victoria M.
ENG: The proposed methodology "MathFISLearn" for the formation of competencies of applicants for the degree "bachelor" in distance learning in the discipline "Mathematical foundations of information security": 1) the study of basic mathematical concepts, theorems and methods in the following sections: the theory of divisibility; theory of decomposition; number theory; the theory of lichens and the theory of algebraic structures during lectures conducted using the "Zoom" system, 2) algorithmization and programming for the implementation of: Euclid's algorithm; extended Euclidean algorithm; Fermat algorithm; decomposition of the number by dividing by sampling; sieve of Eratosthenes; Miller's test and organization of relevant research during laboratory work, 3) acquisition of practical skills in solving systems of equations according to the module based on various mathematical approaches and means when performing independent work using recommended sources, 4) elaboration of theoretical material using lecturer presentations and passing testing in the "Lider" system.
Methods of Forming Competencies in Applicants for the Specialty «Cybersecurity» when Performing a Course Assignment in the Discipline «Local Networks»
(Germany, Karlsruhe: Sergeieva&Co, 2023) Pakhomova, Victoria M.
ENG: The methodology of «AttackDetectionLAN» for the formation of professional and subject competencies of applicants for the degree «Bachelor» in the specialty «Cybersecurity» in the course assignment in the discipline «Local Networks» is proposed: 1) obtaining an idea of the network categories of attacks and the corresponding network classes of attacks; 2) configuration of a multilayer neural network to detect network attacks; 3) creation of a neural model in accordance with the composite structure using the selected neuropackage; 4) on the basis of an open NSL-KDD database, preparation of samples for training and testing of the created neural network; 5) determination of the optimal parameters of the created neural network
Methods of Forming Competencies in Applicants for the Specialty «Cybersecurity» when Performing a Course Assignment in the Discipline «Mathematical Foundation of Information Security»
(ProConferenceOrg in conjunction with Sergeieva&Co, Karlsruhe, Germany, 2024) Pakhomova, Victoria M.
ENG: The methodology of "ComparSystem MathFIS" for the formation of professional and subject competencies of applicants for the degree "Bachelor" in the specialty "Cybersecurity" at fulfillment of the course task in the discipline "Mathematical Foundations of Information Security": 1) getting an idea of the system of comparisons of the first degree; 2) the study of fundamental theorems (in particular, the Chinese remainder theorem); 3) analysis of the control example of the solution systems of comparisons by modules; 4) solving an individual problem using the substitution method and the Chinese remainder theorem; 5) formulation of the relevant conclusion.
Network Traffic Forcasting in Information-telecommunication System of Prydniprovsk Railways Based on Neuro-Fuzzy Network
(Дніпропетровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2016) Pakhomova, Victoria M.
ENG: Purpose. Continuous increase in network traffic in the information-telecommunication system (ITS) of Prydniprovsk Railways leads to the need to determine the real-time network congestion and to control the data flows. One of the possible solutions is a method of forecasting the volume of network traffic (inbound and outbound) using neural network technology that will prevent from server overload and improve the quality of services. Methodology. Analysis of current network traffic in ITS of Prydniprovsk Railways and preparation of sets: learning, test and validation ones was conducted as well as creation of neuro-fuzzy network (hybrid system) in Matlab program and organization of the following phases on the appropriate sets: learning, testing, forecast adequacy analysis. Findings. For the fragment (Dnipropetrovsk – Kyiv) in ITS of Prydniprovsk Railways we made a forecast (day ahead) for volume of network traffic based on the hybrid system created in Matlab program; MAPE values are as follows: 6.9% for volume of inbound traffic; 7.7% for volume of outbound traffic. It was found that the average learning error of the hybrid system decreases in case of increase in: the number of inputs (from 2 to 4); the number of terms (from 2 to 5) of the input variable; learning sample power (from 20 to 100). A significant impact on the average learning error of the hybrid system is caused by the number of terms of its input variable. It was determined that the lowest value of the average learning error is provided by 4-input hybrid system, it ensures more accurate learning of the neuro-fuzzy network by the hybrid method. Originality. The work resulted in the dependences for the average hybrid system error of the network traffic volume forecasting for the fragment (Dnipropetrovsk-Kyiv) in ITS Prydniprovsk Railways on: the number of its inputs, the number of input variable terms, the learning sample power for different learning methods. Practical value. Forecasting of network traffic volume in ITS of Prydniprovsk Railways will allow for real-time identification of the network congestion and control of data flows.
Optimal Route Definition in the Network Based on the Multilayer Neural Model
(Дніпропетровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2018) Pakhomova, Victoria M.; Tsykalo Igor D.
ENG: Purpose. The classic algorithms for finding the shortest path on the graph that underlie existing routing protocols, which are now used in computer networks, in conditions of constant change in network traffic cannot lead to the optimal solution in real time. In this regard, the purpose of the article is to develop a methodology for determining the optimal route in the unified computer network. Methodology. To determine the optimal route in the computer network, the program model "MLP 34-2-410-34" was developed in Python using the TensorFlow framework. It allows toperform the following steps: sample generation (random or balanced); creation of a neural network, the input of which is an array of bandwidth of the computer network channels; training and testing of the neural network in the appropriate samples. Findings. Neural network of 34-2-410-34 configuration with ReLU and Leaky-ReLU activation functions in a hidden layer and the linear activation function in the output layer learns from Adam algorithm. This algorithm is a combination of Adagrad, RMSprop algorithms and stochastic gradient descent with inertia. These functions learn the most quickly in all volumes of the train sample, less than others are subject to reevaluation, and reach the value of the error of 0.0024 on the control sample and in 86% determine the optimal path. Originality. We conducted the study of the neural network parameters based of the calculation of the harmonic mean with different activation functions (Linear, Sigmoid, Tanh, Softplus, ReLU, L-ReLU) on train samples of different volumes (140, 1400, 14000, 49000 examples) and with various neural network training algorithms (BGD, MB SGD, Adam, Adamax, Nadam). Practical value. The use of a neural model, the input of which is an array of channel bandwidth, will allow in real time to determine the optimal route in the computer network.

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